Probabilistic assessment of visual fatigue caused by stereoscopy using dynamic Bayesian networks

Abstract Purpose In this article, we develop a dynamic Bayesian network ( DBN ) model to measure 3D visual fatigue. As far as our information goes, this is the first adaptation of a DBN structure‐based probabilistic framework for inferring the 3D viewer's state of visual fatigue. Methods Our measurement focuses on the interdependencies between each factor and the phenomena of visual fatigue in stereoscopy. Specifically, the implementation of DBN with using multiple features (e.g. contextual, contactless and contact physiological features) and dynamic factor provides a systematic scheme to evaluate 3D visual fatigue. Results In contrast to measurement results between the mean opinion score ( MOS ) and Bayesian network model (with static Bayesian network and DBN ), the visual fatigue in stereoscopy at time slice t is influenced by a dynamic factor (time slice t −1). In the presence of dynamic factors (time slice t −1), our proposed measuring scheme based on DBN is more comprehensive. Conclusion (i) We cover more features for inferring the visual fatigue, more reliably and accurately; (ii) at different time slices, the dynamic factor features are significant for inferring the visual fatigue state of stereoscopy.